DOCSLIB.ORG

RT² Profiler PCR Array (384-Well Format) Human Apoptosis 384HT

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

RT² Profiler PCR Array (384-Well Format) Human Apoptosis 384HT Cat. no. 330231 PAHS-3012ZE For pathway expression analysis Format For use with the following real-time cyclers RT² Profiler PCR Array, Applied Biosystems® models 7900HT (384-well block), Format E ViiA™ 7 (384-well block); Bio-Rad CFX384™ RT² Profiler PCR Array, Roche® LightCycler® 480 (384-well block) Format G Description The Human Apoptosis RT² Profiler PCR Array profiles the expression of 370 key genes involved in apoptosis, or programmed cell death. The array includes the TNF ligands and their receptors; members of the bcl-2 family, BIR (baculoviral IAP repeat) domain proteins, CARD domain (caspase recruitment domain) proteins, death domain proteins, TRAF (TNF receptor-associated factor) domain proteins and caspases. These 370 genes are also grouped by their functional contribution to apoptosis, either anti-apoptosis or induction of apoptosis. Using real-time PCR, you can easily and reliably analyze expression of a focused panel of genes related to apoptosis with this array. For further details, consult the RT² Profiler PCR Array Handbook. Sample & Assay Technologies Shipping and storage RT² Profiler PCR Arrays in formats E and G are shipped at ambient temperature, on dry ice, or blue ice packs depending on destination and accompanying products. For long term storage, keep plates at –20°C. Note: Ensure that you have the correct RT² Profiler PCR Array format for your real-time cycler (see table above). Note: Open the package and store the products appropriately immediately on receipt. Gene table: RT² Profiler PCR Array Position UniGene GenBank Symbol Description A01 Hs.195740 NM_012138 AATF Apoptosis antagonizing transcription factor A02 Hs.431048 NM_005157 ABL1 C-abl oncogene 1, non-receptor tyrosine kinase A03 Hs.438918 NM_004302 ACVR1B Activin A receptor, type IB A04 Hs.77867 NM_000674 ADORA1 Adenosine A1 receptor A05 Hs.424932 NM_004208 AIFM1 Apoptosis-inducing factor, mitochondrion-associated, 1 A06 Hs.655377 NM_032797 AIFM2 Apoptosis-inducing factor, mitochondrion-associated, 2 A07 Hs.163543 NM_144704 AIFM3 Apoptosis-inducing factor, mitochondrion-associated, 3 A08 Hs.525622 NM_005163 AKT1 V-akt murine thymoma viral oncogene homolog 1 A09 Hs.654431 NM_000697 ALOX12 Arachidonate 12-lipoxygenase A10 Hs.111256 NM_001141 ALOX15B Arachidonate 15-lipoxygenase, type B A11 Hs.494173 NM_000700 ANXA1 Annexin A1 A12 Hs.422986 NM_001153 ANXA4 Annexin A4 A13 Hs.728891 NM_001160 APAF1 Apoptotic peptidase activating factor 1 A14 Hs.435771 NM_006595 API5 Apoptosis inhibitor 5 A15 Hs.654439 NM_000041 APOE Apolipoprotein E A16 Hs.9754 NM_012068 ATF5 Activating transcription factor 5 A17 Hs.555966 NM_020371 AVEN Apoptosis, caspase activation inhibitor A18 Hs.72885 NM_001700 AZU1 Azurocidin 1 A19 Hs.370254 NM_004322 BAD BCL2-associated agonist of cell death A20 Hs.377484 NM_004323 BAG1 BCL2-associated athanogene A21 Hs.523309 NM_004281 BAG3 BCL2-associated athanogene 3 A22 Hs.194726 NM_004874 BAG4 BCL2-associated athanogene 4 A23 Hs.485139 NM_001188 BAK1 BCL2-antagonist/killer 1 A24 Hs.624291 NM_004324 BAX BCL2-associated X protein B01 Hs.467020 NM_014417 BBC3 BCL2 binding component 3 B02 Hs.193516 NM_003921 BCL10 B-cell CLL/lymphoma 10 B03 Hs.150749 NM_000633 BCL2 B-cell CLL/lymphoma 2 B04 Hs.227817 NM_004049 BCL2A1 BCL2-related protein A1 B05 Hs.516966 NM_138578 BCL2L1 BCL2-like 1 B06 Hs.283672 NM_020396 BCL2L10 BCL2-like 10 (apoptosis facilitator) B07 Hs.469658 NM_006538 BCL2L11 BCL2-like 11 (apoptosis facilitator) B08 Hs.631672 NM_015367 BCL2L13 BCL2-like 13 (apoptosis facilitator) B09 Hs.410026 NM_004050 BCL2L2 BCL2-like 2 B10 Hs.31210 NM_005178 BCL3 B-cell CLL/lymphoma 3 B11 Hs.478588 NM_001706 BCL6 B-cell CLL/lymphoma 6 B12 Hs.486542 NM_014739 BCLAF1 BCL2-associated transcription factor 1 B13 Hs.502182 NM_001709 BDNF Brain-derived neurotrophic factor B14 Hs.12272 NM_003766 BECN1 Beclin 1, autophagy related B15 Hs.435556 NM_016561 BFAR Bifunctional apoptosis regulator B16 Hs.591054 NM_001196 BID BH3 interacting domain death agonist B17 Hs.475055 NM_001197 BIK BCL2-interacting killer (apoptosis-inducing) B18 Hs.696238 NM_001166 BIRC2 Baculoviral IAP repeat containing 2 B19 Hs.127799 NM_001165 BIRC3 Baculoviral IAP repeat containing 3 B20 Hs.728893 NM_001168 BIRC5 Baculoviral IAP repeat containing 5 B21 Hs.150107 NM_016252 BIRC6 Baculoviral IAP repeat containing 6 B22 Hs.256126 NM_022161 BIRC7 Baculoviral IAP repeat containing 7 B23 Hs.348263 NM_033341 BIRC8 Baculoviral IAP repeat containing 8 B24 Hs.145726 NM_001205 BNIP1 BCL2/adenovirus E1B 19kDa interacting protein 1 C01 Hs.646490 NM_004330 BNIP2 BCL2/adenovirus E1B 19kDa interacting protein 2 C02 Hs.144873 NM_004052 BNIP3 BCL2/adenovirus E1B 19kDa interacting protein 3 Position UniGene GenBank Symbol Description C03 Hs.131226 NM_004331 BNIP3L BCL2/adenovirus E1B 19kDa interacting protein 3-like C04 Hs.293753 NM_032515 BOK BCL2-related ovarian killer C05 Hs.550061 NM_004333 BRAF V-raf murine sarcoma viral oncogene homolog B1 C06 Hs.194143 NM_007294 BRCA1 Breast cancer 1, early onset C07 Hs.258314 NM_004899 BRE Brain and reproductive organ-expressed (TNFRSF1A modulator) C08 Hs.159494 NM_000061 BTK Bruton agammaglobulinemia tyrosine kinase C09 Hs.57973 NM_014550 CARD10 Caspase recruitment domain family, member 10 C10 Hs.648101 NM_032415 CARD11 Caspase recruitment domain family, member 11 C11 Hs.675480 NM_024110 CARD14 Caspase recruitment domain family, member 14 C12 Hs.44102 NM_001007232 CARD17 Caspase recruitment domain family, member 17 C13 Hs.56279 NM_021571 CARD18 Caspase recruitment domain family, member 18 C14 Hs.200242 NM_032587 CARD6 Caspase recruitment domain family, member 6 C15 Hs.694071 NM_052813 CARD9 Caspase recruitment domain family, member 9 C16 Hs.2490 NM_033292 CASP1 Caspase 1, apoptosis-related cysteine peptidase (interleukin 1, beta, convertase) C17 Hs.5353 NM_001230 CASP10 Caspase 10, apoptosis-related cysteine peptidase C18 Hs.466057 NM_012114 CASP14 Caspase 14, apoptosis-related cysteine peptidase C19 Hs.368982 NM_032982 CASP2 Caspase 2, apoptosis-related cysteine peptidase C20 Hs.141125 NM_004346 CASP3 Caspase 3, apoptosis-related cysteine peptidase C21 Hs.138378 NM_001225 CASP4 Caspase 4, apoptosis-related cysteine peptidase C22 Hs.213327 NM_004347 CASP5 Caspase 5, apoptosis-related cysteine peptidase C23 Hs.654616 NM_032992 CASP6 Caspase 6, apoptosis-related cysteine peptidase C24 Hs.9216 NM_001227 CASP7 Caspase 7, apoptosis-related cysteine peptidase D01 Hs.599762 NM_001228 CASP8 Caspase 8, apoptosis-related cysteine peptidase D02 Hs.558218 NM_012115 CASP8AP2 Caspase 8 associated protein 2 D03 Hs.329502 NM_001229 CASP9 Caspase 9, apoptosis-related cysteine peptidase D04 Hs.714363 NM_003655 CBX4 Chromobox homolog 4 D05 Hs.303649 NM_002982 CCL2 Chemokine (C-C motif) ligand 2 D06 Hs.523500 NM_001767 CD2 CD2 molecule D07 Hs.355307 NM_001242 CD27 CD27 molecule D08 Hs.591629 NM_006139 CD28 CD28 molecule D09 Hs.479214 NM_001775 CD38 CD38 molecule D10 Hs.472860 NM_001250 CD40 CD40 molecule, TNF receptor superfamily member 5 D11 Hs.592244 NM_000074 CD40LG CD40 ligand D12 Hs.58685 NM_014207 CD5 CD5 molecule D13 Hs.501497 NM_001252 CD70 CD70 molecule D14 Hs.370771 NM_000389 CDKN1A Cyclin-dependent kinase inhibitor 1A (p21, Cip1) D15 Hs.517106 NM_005194 CEBPB CCAAT/enhancer binding protein (C/EBP), beta D16 Hs.429666 NM_001806 CEBPG CCAAT/enhancer binding protein (C/EBP), gamma D17 Hs.461361 NM_006324 CFDP1 Craniofacial development protein 1 D18 Hs.170622 NM_005507 CFL1 Cofilin 1 (non-muscle) D19 Hs.390736 NM_003879 CFLAR CASP8 and FADD-like apoptosis regulator D20 Hs.291363 NM_007194 CHEK2 CHK2 checkpoint homolog (S. pombe) D21 Hs.249129 NM_001279 CIDEA Cell death-inducing DFFA-like effector a D22 Hs.642693 NM_014430 CIDEB Cell death-inducing DFFA-like effector b D23 Hs.567562 NM_022094 CIDEC Cell death-inducing DFFA-like effector c D24 Hs.570065 NM_000091 COL4A3 Collagen, type IV, alpha 3 (Goodpasture antigen) E01 Hs.38533 NM_003805 CRADD CASP2 and RIPK1 domain containing adaptor with death domain E02 Hs.184085 NM_000394 CRYAA Crystallin, alpha A E03 Hs.408767 NM_001885 CRYAB Crystallin, alpha B E04 Hs.146806 NM_003592 CUL1 Cullin 1 E05 Hs.82919 NM_003591 CUL2 Cullin 2 E06 Hs.372286 NM_003590 CUL3 Cullin 3 E07 Hs.339735 NM_003589 CUL4A Cullin 4A E08 Hs.440320 NM_003478 CUL5 Cullin 5 E09 Hs.437060 NM_018947 CYCS Cytochrome c, somatic E10 Hs.82890 NM_001344 DAD1 Defender against cell death 1 E11 Hs.75189 NM_004394 DAP Death-associated protein E12 Hs.516746 NM_004632 DAP3 Death associated protein 3 E13 Hs.380277 NM_004938 DAPK1 Death-associated protein kinase 1 E14 Hs.237886 NM_014326 DAPK2 Death-associated protein kinase 2 E15 Hs.631844 NM_001348 DAPK3 Death-associated protein kinase 3 E16 Hs.336916 NM_001350 DAXX Death-domain associated protein E17 Hs.414480 NM_001352 DBP D site of albumin promoter (albumin D-box) binding protein E18 Hs.579550 NM_005215 DCC Deleted in colorectal carcinoma E19 Hs.247362 NM_013974 DDAH2 Dimethylarginine dimethylaminohydrolase 2 E20 Hs.728989 NM_004083 DDIT3 DNA-damage-inducible transcript 3 E21 Hs.591405 NM_007204 DDX20 DEAD (Asp-Glu-Ala-Asp) box polypeptide 20 E22 Hs.517342 NM_001039711 DEDD Death effector domain containing E23 Hs.515432 NM_133328 DEDD2 Death effector domain containing 2 E24 Hs.498727 NM_014762 DHCR24 24-dehydrocholesterol reductase F01 Hs.169611 NM_019887 DIABLO Diablo, IAP-binding mitochondrial protein Position UniGene GenBank Symbol Description F02 Hs.13495 NM_006268 DPF2 D4, zinc and double PHD fingers family 2 F03 Hs.173135 NM_003583 DYRK2 Dual-specificity
Recommended publications
  • Cytokine Nomenclature

    Cytokine Nomenclature

    RayBiotech, Inc. The protein array pioneer company Cytokine Nomenclature Cytokine Name Official Full Name Genbank Related Names Symbol 4-1BB TNFRSF Tumor necrosis factor NP_001552 CD137, ILA, 4-1BB ligand receptor 9 receptor superfamily .2. member 9 6Ckine CCL21 6-Cysteine Chemokine NM_002989 Small-inducible cytokine A21, Beta chemokine exodus-2, Secondary lymphoid-tissue chemokine, SLC, SCYA21 ACE ACE Angiotensin-converting NP_000780 CD143, DCP, DCP1 enzyme .1. NP_690043 .1. ACE-2 ACE2 Angiotensin-converting NP_068576 ACE-related carboxypeptidase, enzyme 2 .1 Angiotensin-converting enzyme homolog ACTH ACTH Adrenocorticotropic NP_000930 POMC, Pro-opiomelanocortin, hormone .1. Corticotropin-lipotropin, NPP, NP_001030 Melanotropin gamma, Gamma- 333.1 MSH, Potential peptide, Corticotropin, Melanotropin alpha, Alpha-MSH, Corticotropin-like intermediary peptide, CLIP, Lipotropin beta, Beta-LPH, Lipotropin gamma, Gamma-LPH, Melanotropin beta, Beta-MSH, Beta-endorphin, Met-enkephalin ACTHR ACTHR Adrenocorticotropic NP_000520 Melanocortin receptor 2, MC2-R hormone receptor .1 Activin A INHBA Activin A NM_002192 Activin beta-A chain, Erythroid differentiation protein, EDF, INHBA Activin B INHBB Activin B NM_002193 Inhibin beta B chain, Activin beta-B chain Activin C INHBC Activin C NM005538 Inhibin, beta C Activin RIA ACVR1 Activin receptor type-1 NM_001105 Activin receptor type I, ACTR-I, Serine/threonine-protein kinase receptor R1, SKR1, Activin receptor-like kinase 2, ALK-2, TGF-B superfamily receptor type I, TSR-I, ACVRLK2 Activin RIB ACVR1B
  • Table 2. Significant

    Table 2. Significant

    Table 2. Significant (Q < 0.05 and |d | > 0.5) transcripts from the meta-analysis Gene Chr Mb Gene Name Affy ProbeSet cDNA_IDs d HAP/LAP d HAP/LAP d d IS Average d Ztest P values Q-value Symbol ID (study #5) 1 2 STS B2m 2 122 beta-2 microglobulin 1452428_a_at AI848245 1.75334941 4 3.2 4 3.2316485 1.07398E-09 5.69E-08 Man2b1 8 84.4 mannosidase 2, alpha B1 1416340_a_at H4049B01 3.75722111 3.87309653 2.1 1.6 2.84852656 5.32443E-07 1.58E-05 1110032A03Rik 9 50.9 RIKEN cDNA 1110032A03 gene 1417211_a_at H4035E05 4 1.66015788 4 1.7 2.82772795 2.94266E-05 0.000527 NA 9 48.5 --- 1456111_at 3.43701477 1.85785922 4 2 2.8237185 9.97969E-08 3.48E-06 Scn4b 9 45.3 Sodium channel, type IV, beta 1434008_at AI844796 3.79536664 1.63774235 3.3 2.3 2.75319499 1.48057E-08 6.21E-07 polypeptide Gadd45gip1 8 84.1 RIKEN cDNA 2310040G17 gene 1417619_at 4 3.38875643 1.4 2 2.69163229 8.84279E-06 0.0001904 BC056474 15 12.1 Mus musculus cDNA clone 1424117_at H3030A06 3.95752801 2.42838452 1.9 2.2 2.62132809 1.3344E-08 5.66E-07 MGC:67360 IMAGE:6823629, complete cds NA 4 153 guanine nucleotide binding protein, 1454696_at -3.46081884 -4 -1.3 -1.6 -2.6026947 8.58458E-05 0.0012617 beta 1 Gnb1 4 153 guanine nucleotide binding protein, 1417432_a_at H3094D02 -3.13334396 -4 -1.6 -1.7 -2.5946297 1.04542E-05 0.0002202 beta 1 Gadd45gip1 8 84.1 RAD23a homolog (S.
  • A Minimal Set of Internal Control Genes for Gene Expression Studies in Head

    A Minimal Set of Internal Control Genes for Gene Expression Studies in Head

    bioRxiv preprint doi: https://doi.org/10.1101/108381; this version posted February 14, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 A minimal set of internal control genes for gene expression studies in head 2 and neck squamous cell carcinoma. 1 1 1 2 3 Vinayak Palve , Manisha Pareek , Neeraja M Krishnan , Gangotri Siddappa , 2 2 1,3* 4 Amritha Suresh , Moni Abraham Kuriakose and Binay Panda 5 1 6 Ganit Labs, Bio-IT Centre, Institute of Bioinformatics and Applied Biotechnology, 7 Bangalore 560100 2 8 Mazumdar Shaw Cancer Centre and Mazumdar Shaw Centre for Translational 9 Research, Bangalore 560096 3 10 Strand Life Sciences, Bangalore 560024 11 * Corresponding author: [email protected] 12 13 Abstract: 14 Background: Selection of the right reference gene(s) is crucial in the analysis and 15 interpretation of gene expression data. In head and neck cancer, studies evaluating 16 the efficacy of internal reference genes are rare. Here, we present data for a minimal 17 set of candidates as internal control genes for gene expression studies in head and 18 neck cancer. 19 Methods: We analyzed data from multiple sources (in house whole-genome gene 20 expression microarrays, n=21; TCGA RNA-seq, n=42, and published gene 21 expression studies in head and neck tumors from literature) to come up with a set of 22 genes (discovery set) for their stable expression across tumor and normal tissues.
  • Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia

    Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia

    Letter to the Editor http://dx.doi.org/10.5021/ad.2015.27.4.474 Mutations in EDA and EDAR Genes in a Large Mexican Hispanic Cohort with Hypohidrotic Ectodermal Dysplasia Julio C Salas-Alanis1,2, Eva Wozniak3, Charles A Mein3, Carola C Duran Mckinster4, Jorge Ocampo-Candiani1, David P Kelsell5, Rong Hua6, Maria L Garza-Rodriguez7, Keith A Choate6, Hugo A Barrera Saldaña7,8 1Dermatology Department, Hospital Universitario, Universidad Autonoma de Nuevo Leon, 2Basic Science Department, Medicine School, Universidad de Monterrey, Monterrey, NL, Mexico, 3Barts and the London Genome Centre, John Vane Science Centre, Barts and the London School of Medicine and Dentistry, University of London, London, United Kingdom, 4Instituto Nacional de Pediatria, Coyoacan, CP, Mexico, 5Centre for Cutaneous Research, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom, 6Departments of Dermatology, Genetics, and Pathology, Yale University School of Medicine, New Haven, CT, USA, 7Facultad de Medicina, Departamento de Bioquímica y Medicina Molecular, Universidad Autonoma de Nuevo Leon,8Vitagenesis, Monterrey, NL, Mexico Dear Editor: cludes dryness of the skin, eyes, airways, and mucous Ectodermal dysplasias (ED) encompass nearly 200 differ- membranes, as well as other ectodermal defects and, in ent genetic conditions identified by the lack, or dysgenesis, some cases, fever, seizures, and rarely, death. of at least two ectodermal derivatives, such as hair, nails, XL-HED is caused by mutations in the EDA gene, located teeth, and sweat glands. Hypohidrotic/anhidrotic ED (HED) on chromosome Xq12-q13.1, which encodes a signaling is the most frequent form of ED and it can be inherited as molecule of the tumor necrosis factor (TNF) superfamily.
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • Functional Analysis of Ectodysplasin-A Mutations in X-Linked Non-Syndromic Hypodontia and Possible Involvement of X-Chromosome Inactivation

    Functional Analysis of Ectodysplasin-A Mutations in X-Linked Non-Syndromic Hypodontia and Possible Involvement of X-Chromosome Inactivation

    Functional analysis of Ectodysplasin-A mutations in X-linked non-syndromic hypodontia and possible involvement of X-chromosome inactivation Yuhua Pan ( [email protected] ) Southern Medical University https://orcid.org/0000-0001-8039-1627 Ting Lu Southern Medical University Ling Peng Southern Medical University Qi Zeng Southern Medical University Xiangyu Huang Southern Medical University Buling Wu Southern Medical University Fu Xiong Southern Medical University Research Article Keywords: ectodysplasin-A, dental pulp stem cells, X chromosome inactivation, tooth agenesis, odontogenic differentiation Posted Date: August 13th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-602940/v2 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/17 Abstract Background: Mutations of the Ectodysplasin-A (EDA) gene are generally associated with syndrome hypohidrotic ectodermal dysplasia or non-syndromic tooth agenesis. The inuence of EDA mutations on dentinogenesis and odontoblast differentiation have not been reported. The aim of this study was to identify genetic clues for the causes of familial non-syndromic oligodontia and explore the underlying mechanisms involved, while focusing on the role of human dental pulp stem cells (hDPSCs). Materials and Methods: Candidate gene sequences were obtained by PCR amplication and Sanger sequencing. Functional analysis was conducted, and the pathogenesis associated with EDA mutations in hDPSCs was investigated to explore the impact of the identied mutation on the phenotype. Capillary electrophoresis (CE) was used to detect X chromosome inactivation (XCI) in the blood of female carriers. Results: In this study, we identied an EDA mutation in a Chinese familythe missense mutation c.1013C>T (Thr338Met).
  • Upregulation Mitochondrial Carrier 1 (MTCH1) Is Associated with Cell Proliferation, Invasion, and Migration of Liver Hepatocellular Carcinoma

    Upregulation Mitochondrial Carrier 1 (MTCH1) Is Associated with Cell Proliferation, Invasion, and Migration of Liver Hepatocellular Carcinoma

    Hindawi BioMed Research International Volume 2021, Article ID 9911784, 10 pages https://doi.org/10.1155/2021/9911784 Research Article Upregulation Mitochondrial Carrier 1 (MTCH1) Is Associated with Cell Proliferation, Invasion, and Migration of Liver Hepatocellular Carcinoma Guolin Chen ,1 Shanshan Mo,2 and Di Yuan3 1Department of Infectious Diseases, The First Affiliated Hospital of Harbin Medical University, Harbin, China 2Pharmacy Department of Heilongjiang Sailors General Hospital, Harbin, China 3Clinical Laboratory, The First Affiliated Hospital of Harbin Medical University, Harbin, China Correspondence should be addressed to Guolin Chen; [email protected] Received 27 March 2021; Accepted 25 May 2021; Published 7 June 2021 Academic Editor: Tao Huang Copyright © 2021 Guolin Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Among the primary causes of cancer-associated death in the world, liver hepatocellular carcinoma (LIHC) ranks the third. LIHC is defined as the sixth most frequently diagnosed carcinoma. The gene mitochondrial carrier 1 (MTCH1) is a protein-coding gene. Recent research suggests that MTCH1 may be associated with some diseases. Here, our study attempts to explore the role and implication of MTCH1 in LIHC. Kaplan Meier Plotter and GEPIA (Gene Expression Profiling Interactive Analysis) databases were employed to determine the expression of MTCH1 and its correlation with prognostic status in LIHC patients. For the first time, our results suggested that MTCH1 was aberrantly expressed in human pan-cancer and highly expressed in LIHC. Its high expression was closely associated with metastasis of tumor, stage of cancer, and poor survival of patients.
  • Cellular and Molecular Signatures in the Disease Tissue of Early

    Cellular and Molecular Signatures in the Disease Tissue of Early

    Cellular and Molecular Signatures in the Disease Tissue of Early Rheumatoid Arthritis Stratify Clinical Response to csDMARD-Therapy and Predict Radiographic Progression Frances Humby1,* Myles Lewis1,* Nandhini Ramamoorthi2, Jason Hackney3, Michael Barnes1, Michele Bombardieri1, Francesca Setiadi2, Stephen Kelly1, Fabiola Bene1, Maria di Cicco1, Sudeh Riahi1, Vidalba Rocher-Ros1, Nora Ng1, Ilias Lazorou1, Rebecca E. Hands1, Desiree van der Heijde4, Robert Landewé5, Annette van der Helm-van Mil4, Alberto Cauli6, Iain B. McInnes7, Christopher D. Buckley8, Ernest Choy9, Peter Taylor10, Michael J. Townsend2 & Costantino Pitzalis1 1Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Departments of 2Biomarker Discovery OMNI, 3Bioinformatics and Computational Biology, Genentech Research and Early Development, South San Francisco, California 94080 USA 4Department of Rheumatology, Leiden University Medical Center, The Netherlands 5Department of Clinical Immunology & Rheumatology, Amsterdam Rheumatology & Immunology Center, Amsterdam, The Netherlands 6Rheumatology Unit, Department of Medical Sciences, Policlinico of the University of Cagliari, Cagliari, Italy 7Institute of Infection, Immunity and Inflammation, University of Glasgow, Glasgow G12 8TA, UK 8Rheumatology Research Group, Institute of Inflammation and Ageing (IIA), University of Birmingham, Birmingham B15 2WB, UK 9Institute of
  • Molecular Effects of Isoflavone Supplementation Human Intervention Studies and Quantitative Models for Risk Assessment

    Molecular Effects of Isoflavone Supplementation Human Intervention Studies and Quantitative Models for Risk Assessment

    Molecular effects of isoflavone supplementation Human intervention studies and quantitative models for risk assessment Vera van der Velpen Thesis committee Promotors Prof. Dr Pieter van ‘t Veer Professor of Nutritional Epidemiology Wageningen University Prof. Dr Evert G. Schouten Emeritus Professor of Epidemiology and Prevention Wageningen University Co-promotors Dr Anouk Geelen Assistant professor, Division of Human Nutrition Wageningen University Dr Lydia A. Afman Assistant professor, Division of Human Nutrition Wageningen University Other members Prof. Dr Jaap Keijer, Wageningen University Dr Hubert P.J.M. Noteborn, Netherlands Food en Consumer Product Safety Authority Prof. Dr Yvonne T. van der Schouw, UMC Utrecht Dr Wendy L. Hall, King’s College London This research was conducted under the auspices of the Graduate School VLAG (Advanced studies in Food Technology, Agrobiotechnology, Nutrition and Health Sciences). Molecular effects of isoflavone supplementation Human intervention studies and quantitative models for risk assessment Vera van der Velpen Thesis submitted in fulfilment of the requirements for the degree of doctor at Wageningen University by the authority of the Rector Magnificus Prof. Dr M.J. Kropff, in the presence of the Thesis Committee appointed by the Academic Board to be defended in public on Friday 20 June 2014 at 13.30 p.m. in the Aula. Vera van der Velpen Molecular effects of isoflavone supplementation: Human intervention studies and quantitative models for risk assessment 154 pages PhD thesis, Wageningen University, Wageningen, NL (2014) With references, with summaries in Dutch and English ISBN: 978-94-6173-952-0 ABSTRact Background: Risk assessment can potentially be improved by closely linked experiments in the disciplines of epidemiology and toxicology.
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
  • Supplementary Table 8. Cpcp PPI Network Details for Significantly Changed Proteins, As Identified in 3.2, Underlying Each of the Five Functional Domains

    Supplementary Table 8. Cpcp PPI Network Details for Significantly Changed Proteins, As Identified in 3.2, Underlying Each of the Five Functional Domains

    Supplementary Table 8. cPCP PPI network details for significantly changed proteins, as identified in 3.2, underlying each of the five functional domains. The network nodes represent each significant protein, followed by the list of interactors. Note that identifiers were converted to gene names to facilitate PPI database queries. Functional Domain Node Interactors Development and Park7 Rack1 differentiation Kcnma1 Atp6v1a Ywhae Ywhaz Pgls Hsd3b7 Development and Prdx6 Ncoa3 differentiation Pla2g4a Sufu Ncf2 Gstp1 Grin2b Ywhae Pgls Hsd3b7 Development and Atp1a2 Kcnma1 differentiation Vamp2 Development and Cntn1 Prnp differentiation Ywhaz Clstn1 Dlg4 App Ywhae Ywhab Development and Rac1 Pak1 differentiation Cdc42 Rhoa Dlg4 Ctnnb1 Mapk9 Mapk8 Pik3cb Sod1 Rrad Epb41l2 Nono Ltbp1 Evi5 Rbm39 Aplp2 Smurf2 Grin1 Grin2b Xiap Chn2 Cav1 Cybb Pgls Ywhae Development and Hbb-b1 Atp5b differentiation Hba Kcnma1 Got1 Aldoa Ywhaz Pgls Hsd3b4 Hsd3b7 Ywhae Development and Myh6 Mybpc3 differentiation Prkce Ywhae Development and Amph Capn2 differentiation Ap2a2 Dnm1 Dnm3 Dnm2 Atp6v1a Ywhab Development and Dnm3 Bin1 differentiation Amph Pacsin1 Grb2 Ywhae Bsn Development and Eef2 Ywhaz differentiation Rpgrip1l Atp6v1a Nphp1 Iqcb1 Ezh2 Ywhae Ywhab Pgls Hsd3b7 Hsd3b4 Development and Gnai1 Dlg4 differentiation Development and Gnao1 Dlg4 differentiation Vamp2 App Ywhae Ywhab Development and Psmd3 Rpgrip1l differentiation Psmd4 Hmga2 Development and Thy1 Syp differentiation Atp6v1a App Ywhae Ywhaz Ywhab Hsd3b7 Hsd3b4 Development and Tubb2a Ywhaz differentiation Nphp4
  • A Rare Intronic Variation of Presenilin-1 (Rs201992645)

    A Rare Intronic Variation of Presenilin-1 (Rs201992645)

    tics: Cu ne rr e en G t y R r e a t s i e d a e r Bhaumik, et al., Hereditary Genet 2014, 3:3 r c e h H Hereditary Genetics DOI: 10.4172/2161-1041.1000136 ISSN: 2161-1041 Research Article Open Access A Rare Intronic Variation of Presenilin-1 (rs201992645) is Associated with Alzheimer’s Disease and Down Syndrome Birth Pranami Bhaumik1, Priyanka Ghosh1, Sujoy Ghosh2, Kausik Majumdar3, Sandip Pal4, Biswanath Sarkar5, AbhishiktaGhosh Roy5, Abhinandan Chakraborty1 and Subrata Kumar Dey1* 1Human genetics Research Unit, Department of Biotechnology, School of Biotechnology and Biological Sciences, West Bengal University of Technology, India 2Genetics & Genomics Research Unit, Department of Zoology, University of Calcutta, (Ballygunge Science college campus), India 3Department of Elderly Medicine, National Neurosciences Centre/Peerless Hospital, Panchasayar, India 4Department of Neurology, Burdwan Medical College, India 5DNA laboratory, Anthropological Survey of India, 27 Jawaharlal Nehru Road Kolkata, India *Corresponding author: Subrata Kumar Dey, Human genetics Research Unit, Department of Biotechnology, School of Biotechnology and Biological Sciences, West Bengal University of Technology, BF – 142, Salt Lake City, Sector I. Kolkata, West Bengal, India, Tel: +919830278216; Fax: (033) 2334 1030;[email protected] Rec Date: Jun 15, 2014, Acc Date: Sep 30, 2014, Pub Date: Oct 08, 2014 Copyright: © 2014, Bahumik P et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract Background and objective: Presenilin-1 (PSEN-1) gene is a potent candidate that relates Alzheimer’s disease (AD) to Down syndrome (DS).